Big data is hot these days, so it should come as no surprise that big data startups are crawling out of the woodworks. And venture capitalists, in turn, are paying attention. But getting that attention and the funding that comes with it requires more than simply adding the words "big data" to whatever you happen to be pitching.

"Anyone who walks in and says, 'I am a big data company,' is not a big data company, says Shivon Zilis of Bloomberg Beta, speaking on an early-stage VC panel at the GigaOm Structure Data event in New York City this week.

The VCs on the panel made it clear: No buzzwords, no pitches by analogy (e.g., "we're like github, but for data scientists!") and no "Hadoop in the cloud." In fact, it's no longer really about data infrastructure. Instead, the panelists agreed, it's about the application layer and data products.

"What data gives us that we didn't have before is the capability to know things about the world that were otherwise outside our perception," says Hilary Mason, a data scientist and computer scientist that advises Accel Partners. "This robust data infrastructure is now allowing exciting data products."

Mason points to one of her favorite big data applications, Dark Sky a smart phone app that predicts precipitation - down to the minute - at your location and alerts you before it starts.

"I'm most interested in data products," adds Sven Strohband, CTO of Khosla Ventures. "All of this started really in the enterprise and on the consumer side for consumer products, but I see it now in industries where you wouldn't expect it."

He points to agriculture, where sensors and big data technologies are now being used in sophisticated ways for precision agriculture by blending soil composition data, elevation data, climate and weather data, seed type data and more to optimize crop yields. Zilis adds that water optimization as a result of imaging technology is an interest, but so are all manner of vertical applications in areas like the insurance and retail finance industries.

"It's not always big data, it's being more creative with data," she says. "Data's going to eat all industries and verticals. For us, it's really about understanding which of them get affected first."

One of the key things the VCs agree they look for in a startup is an entrepreneur with deep domain expertise in a particular field. Zilis adds that the "magic combo" she often looks for is a domain expert with years of experience who is paired with a technologist from outside business who isn't locked into a particular way of doing things because that's how it's always been done.

"I'm finding you can attract really great data scientists to ideas where you're applying the technology they've been working on to a great idea," Jobanputra says. "The technology definitely has to be there, but making sure you have the right expertise and are addressing the right pain points is really important."

Mason adds that big data startups need to make sure they remember the 'science' part of 'data science.'

"Most companies say they're data driven, but they're actually using their data very poorly," she says, noting that they collect data but then optimize it, subverting the scientific method. "They don't really sink their mind into the scientific method. They lack an experimental process."